{
  "nbformat": 4,
  "nbformat_minor": 0,
  "metadata": {
    "colab": {
      "name": "Lorenz_inverse_forced_Colab.ipynb",
      "provenance": [],
      "authorship_tag": "ABX9TyPifhOVOSE/AZTj2ZsM+CeE",
      "include_colab_link": true
    },
    "kernelspec": {
      "name": "python3",
      "display_name": "Python 3"
    }
  },
  "cells": [
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "view-in-github",
        "colab_type": "text"
      },
      "source": [
        "<a href=\"https://colab.research.google.com/github/lululxvi/deepxde/blob/master/examples/Lorenz_inverse_forced_Colab.ipynb\" target=\"_parent\"><img src=\"https://colab.research.google.com/assets/colab-badge.svg\" alt=\"Open In Colab\"/></a>"
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "se3D-wVRKqIO"
      },
      "source": [
        "# Description\n",
        "\n",
        "This notebook aims at the identification of the parameters of the modified Lorenz attractor (with exogenous input)\n",
        "\n",
        "Built upon: \n",
        "* Lorenz attractor example from DeepXDE (Lu's code)\n",
        "* https://github.com/lululxvi/deepxde/issues/79\n",
        "* kind help from Lu, greatly acknowledged\n",
        "\n",
        "# Install lib and imports"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "FZZFhzM5KAI-",
        "outputId": "e42dc0bb-75c2-4c1c-a0e0-272a13a81736",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "!pip install deepxde\n",
        "\n",
        "from __future__ import absolute_import\n",
        "from __future__ import division\n",
        "from __future__ import print_function\n",
        "\n",
        "import re\n",
        "import numpy as np\n",
        "import requests\n",
        "import io\n",
        "import matplotlib.pyplot as plt\n",
        "\n",
        "import deepxde as dde\n",
        "from deepxde.backend import tf\n",
        "\n",
        "import scipy as sp\n",
        "import scipy.interpolate as interp\n",
        "from scipy.integrate import odeint\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "stream",
          "text": [
            "Collecting deepxde\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/cf/8e/ee29b85e5892f9775e68a7842a3e808a3d935e2731a00c2ef5f47579b195/DeepXDE-0.8.5-py3-none-any.whl (67kB)\n",
            "\u001b[K     |████████████████████████████████| 71kB 2.2MB/s \n",
            "\u001b[?25hCollecting salib\n",
            "\u001b[?25l  Downloading https://files.pythonhosted.org/packages/f7/33/cee4d64f7c40f33c08cf5ef5c9b1fb5e51f194b5deceefb5567112800b70/SALib-1.3.11.tar.gz (856kB)\n",
            "\u001b[K     |████████████████████████████████| 860kB 8.7MB/s \n",
            "\u001b[?25hRequirement already satisfied: tensorflow>=1.14.0 in /usr/local/lib/python3.6/dist-packages (from deepxde) (2.3.0)\n",
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            "Building wheels for collected packages: salib\n",
            "  Building wheel for salib (setup.py) ... \u001b[?25l\u001b[?25hdone\n",
            "  Created wheel for salib: filename=SALib-1.3.11-py2.py3-none-any.whl size=729665 sha256=959eb24383de6204e4fb3a66a5e7d8ee7ab00adb5e306e70995ee8d6dac9a325\n",
            "  Stored in directory: /root/.cache/pip/wheels/62/ed/f9/a0b98754ffb2191b98324b96cbbeb1bd5d9598b39ab996b429\n",
            "Successfully built salib\n",
            "Installing collected packages: salib, deepxde\n",
            "Successfully installed deepxde-0.8.5 salib-1.3.11\n",
            "Using TensorFlow 2 backend.\n",
            "\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/compat/v2_compat.py:96: disable_resource_variables (from tensorflow.python.ops.variable_scope) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "non-resource variables are not supported in the long term\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "dtUaqu5QLjcg"
      },
      "source": [
        "# Generate data"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "TJv1Z6RXLlk1",
        "outputId": "f3fdd8dc-9e44-43be-90bb-ff488580da57",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 296
        }
      },
      "source": [
        "# true values, see p. 15 in https://arxiv.org/abs/1907.04502\n",
        "C1true = 10\n",
        "C2true = 15\n",
        "C3true = 8 / 3\n",
        "\n",
        "# time points\n",
        "maxtime = 3\n",
        "time = np.linspace(0, maxtime, 200)\n",
        "ex_input = 10 * np.sin(2 * np.pi * time)  # exogenous input\n",
        "\n",
        "# interpolate time / lift vectors (for using exogenous variable without fixed time stamps)\n",
        "def ex_func(t):\n",
        "    spline = sp.interpolate.Rbf(\n",
        "        time, ex_input, function=\"thin_plate\", smooth=0, episilon=0\n",
        "    )\n",
        "    # return spline(t[:,0:])\n",
        "    return spline(t)\n",
        "\n",
        "\n",
        "# function that returns dy/dt\n",
        "def LorezODE(x, t):  # Modified Lorenz system (with exogenous input).\n",
        "    x1, x2, x3 = x\n",
        "    dxdt = [\n",
        "        C1true * (x2 - x1),\n",
        "        x1 * (C2true - x3) - x2,\n",
        "        x1 * x2 - C3true * x3 + ex_func(t),\n",
        "    ]\n",
        "    return dxdt\n",
        "\n",
        "\n",
        "# initial condition\n",
        "x0 = [-8, 7, 27]\n",
        "\n",
        "# solve ODE\n",
        "x = odeint(LorezODE, x0, time)\n",
        "\n",
        "# plot results\n",
        "plt.plot(time, x, time, ex_input)\n",
        "plt.xlabel(\"time\")\n",
        "plt.ylabel(\"x(t)\")\n",
        "plt.show()\n",
        "\n",
        "time = time.reshape(-1, 1)\n",
        "time.shape\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "execute_result",
          "data": {
            "text/plain": [
              "(200, 1)"
            ]
          },
          "metadata": {
            "tags": []
          },
          "execution_count": 2
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "rcdsJyuTLuvC"
      },
      "source": [
        "# Perform identification"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "-cDKvMGOETYM",
        "outputId": "96da8367-290b-41e2-b38d-4c31ab5ae057",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 1000
        }
      },
      "source": [
        "# parameters to be identified\n",
        "C1 = tf.Variable(1.0)\n",
        "C2 = tf.Variable(1.0)\n",
        "C3 = tf.Variable(1.0)\n",
        "\n",
        "# interpolate time / lift vectors (for using exogenous variable without fixed time stamps)\n",
        "def ex_func2(t):\n",
        "    spline = sp.interpolate.Rbf(\n",
        "        time, ex_input, function=\"thin_plate\", smooth=0, episilon=0\n",
        "    )\n",
        "    return spline(t[:, 0:])\n",
        "    # return spline(t)\n",
        "\n",
        "\n",
        "# define system ODEs\n",
        "def Lorenz_system(x, y, ex):\n",
        "    \"\"\"Modified Lorenz system (with exogenous input).\n",
        "    dy1/dx = 10 * (y2 - y1)\n",
        "    dy2/dx = y1 * (28 - y3) - y2\n",
        "    dy3/dx = y1 * y2 - 8/3 * y3 + u\n",
        "    \"\"\"\n",
        "    y1, y2, y3 = y[:, 0:1], y[:, 1:2], y[:, 2:]\n",
        "    dy1_x = dde.grad.jacobian(y, x, i=0)\n",
        "    dy2_x = dde.grad.jacobian(y, x, i=1)\n",
        "    dy3_x = dde.grad.jacobian(y, x, i=2)\n",
        "    return [\n",
        "        dy1_x - C1 * (y2 - y1),\n",
        "        dy2_x - y1 * (C2 - y3) + y2,\n",
        "        dy3_x - y1 * y2 + C3 * y3 - ex,\n",
        "        # dy3_x - y1 * y2 + C3 * y3 - 10*tf.math.sin(2*np.pi*x),\n",
        "    ]\n",
        "\n",
        "\n",
        "def boundary(_, on_initial):\n",
        "    return on_initial\n",
        "\n",
        "\n",
        "# define time domain\n",
        "geom = dde.geometry.TimeDomain(0, maxtime)\n",
        "\n",
        "# Initial conditions\n",
        "ic1 = dde.IC(geom, lambda X: x0[0], boundary, component=0)\n",
        "ic2 = dde.IC(geom, lambda X: x0[1], boundary, component=1)\n",
        "ic3 = dde.IC(geom, lambda X: x0[2], boundary, component=2)\n",
        "\n",
        "# Get the training data\n",
        "observe_t, ob_y = time, x\n",
        "# boundary conditions\n",
        "observe_y0 = dde.PointSetBC(observe_t, ob_y[:, 0:1], component=0)\n",
        "observe_y1 = dde.PointSetBC(observe_t, ob_y[:, 1:2], component=1)\n",
        "observe_y2 = dde.PointSetBC(observe_t, ob_y[:, 2:3], component=2)\n",
        "\n",
        "# define data object\n",
        "data = dde.data.PDE(\n",
        "    geom,\n",
        "    Lorenz_system,\n",
        "    [ic1, ic2, ic3, observe_y0, observe_y1, observe_y2],\n",
        "    num_domain=400,\n",
        "    num_boundary=2,\n",
        "    anchors=observe_t,\n",
        "    auxiliary_var_function=ex_func2,\n",
        ")\n",
        "\n",
        "plt.plot(observe_t, ob_y)\n",
        "plt.xlabel(\"Time\")\n",
        "plt.legend([\"x\", \"y\", \"z\"])\n",
        "plt.title(\"Training data\")\n",
        "plt.show()\n",
        "\n",
        "# define FNN architecture and compile\n",
        "net = dde.maps.FNN([1] + [40] * 3 + [3], \"tanh\", \"Glorot uniform\")\n",
        "model = dde.Model(data, net)\n",
        "model.compile(\"adam\", lr=0.001)\n",
        "\n",
        "# callbacks for storing results\n",
        "fnamevar = \"variables.dat\"\n",
        "variable = dde.callbacks.VariableValue([C1, C2, C3], period=1, filename=fnamevar)\n",
        "\n",
        "losshistory, train_state = model.train(epochs=60000, callbacks=[variable])\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Compiling model...\n",
            "Building feed-forward neural network...\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/deepxde/maps/fnn.py:82: dense (from tensorflow.python.keras.legacy_tf_layers.core) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Use keras.layers.Dense instead.\n",
            "WARNING:tensorflow:From /usr/local/lib/python3.6/dist-packages/tensorflow/python/keras/legacy_tf_layers/core.py:187: Layer.apply (from tensorflow.python.keras.engine.base_layer_v1) is deprecated and will be removed in a future version.\n",
            "Instructions for updating:\n",
            "Please use `layer.__call__` method instead.\n",
            "'build' took 0.074330 s\n",
            "\n",
            "'compile' took 1.289168 s\n",
            "\n",
            "Initializing variables...\n",
            "Training model...\n",
            "\n",
            "Step      Train loss                                                                                    Test loss                                                                                     Test metric\n",
            "0         [1.16e-01, 1.51e-01, 5.04e+01, 6.40e+01, 4.90e+01, 7.29e+02, 2.70e+01, 3.29e+01, 2.07e+02]    [1.16e-01, 1.51e-01, 5.04e+01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "1000      [5.36e+00, 3.01e+00, 7.93e+00, 3.42e+01, 6.84e-03, 2.17e+00, 2.97e+01, 1.55e+01, 2.68e+01]    [5.36e+00, 3.01e+00, 7.93e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "2000      [9.25e+00, 2.24e+00, 3.39e+00, 1.85e+01, 3.10e-01, 7.63e-01, 2.82e+01, 2.24e+01, 2.31e+01]    [9.25e+00, 2.24e+00, 3.39e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "3000      [9.38e+00, 2.25e+00, 2.95e+00, 1.40e+01, 3.20e-01, 6.52e-01, 2.75e+01, 2.56e+01, 2.14e+01]    [9.38e+00, 2.25e+00, 2.95e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "4000      [8.93e+00, 2.57e+00, 2.65e+00, 1.10e+01, 2.91e-01, 6.16e-01, 2.68e+01, 2.78e+01, 2.02e+01]    [8.93e+00, 2.57e+00, 2.65e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "5000      [7.69e+00, 2.94e+00, 1.04e+00, 7.65e+00, 3.00e-01, 7.34e-01, 2.60e+01, 2.97e+01, 1.89e+01]    [7.69e+00, 2.94e+00, 1.04e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "6000      [6.76e+00, 4.01e+00, 7.93e-01, 5.69e+00, 2.95e-01, 6.13e-01, 2.34e+01, 2.97e+01, 1.97e+01]    [6.76e+00, 4.01e+00, 7.93e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "7000      [3.06e+00, 3.00e+00, 1.45e+00, 2.48e+00, 2.98e-01, 6.28e-02, 9.13e+00, 1.36e+01, 2.56e+01]    [3.06e+00, 3.00e+00, 1.45e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "8000      [1.78e+00, 2.86e+00, 1.42e+00, 1.11e+00, 2.44e-01, 1.01e-02, 5.65e+00, 5.65e+00, 1.84e+01]    [1.78e+00, 2.86e+00, 1.42e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "9000      [7.07e-01, 1.45e+00, 8.50e-01, 4.97e-01, 4.36e-02, 1.64e-02, 3.23e+00, 2.63e+00, 1.22e+01]    [7.07e-01, 1.45e+00, 8.50e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "10000     [3.39e-01, 9.40e-01, 5.44e-01, 2.18e-01, 1.25e-02, 2.11e-02, 2.00e+00, 1.55e+00, 7.96e+00]    [3.39e-01, 9.40e-01, 5.44e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "11000     [1.72e-01, 6.08e-01, 3.68e-01, 1.04e-01, 7.33e-03, 1.28e-02, 1.22e+00, 8.97e-01, 4.79e+00]    [1.72e-01, 6.08e-01, 3.68e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "12000     [9.48e-02, 3.65e-01, 2.24e-01, 5.23e-02, 3.93e-03, 7.96e-03, 7.14e-01, 4.94e-01, 2.56e+00]    [9.48e-02, 3.65e-01, 2.24e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "13000     [5.29e-02, 1.96e-01, 1.23e-01, 2.55e-02, 1.74e-03, 5.37e-03, 3.85e-01, 2.51e-01, 1.15e+00]    [5.29e-02, 1.96e-01, 1.23e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "14000     [3.29e-02, 9.93e-02, 6.71e-02, 1.17e-02, 4.72e-04, 3.37e-03, 1.89e-01, 1.16e-01, 4.03e-01]    [3.29e-02, 9.93e-02, 6.71e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "15000     [2.19e-02, 5.24e-02, 3.36e-02, 5.21e-03, 7.10e-05, 2.17e-03, 8.11e-02, 4.82e-02, 1.05e-01]    [2.19e-02, 5.24e-02, 3.36e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "16000     [1.66e-02, 3.17e-02, 2.44e-02, 2.34e-03, 1.85e-07, 7.33e-04, 3.03e-02, 1.86e-02, 2.43e-02]    [1.66e-02, 3.17e-02, 2.44e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "17000     [3.71e-02, 1.03e-01, 2.08e-01, 1.76e-04, 2.94e-05, 6.99e-04, 8.99e-03, 5.04e-03, 7.57e-03]    [3.71e-02, 1.03e-01, 2.08e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "18000     [1.26e-02, 2.23e-02, 1.49e-02, 5.24e-05, 6.61e-05, 2.11e-04, 3.14e-03, 1.66e-03, 2.28e-03]    [1.26e-02, 2.23e-02, 1.49e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "19000     [1.51e-02, 2.17e-02, 1.67e-02, 2.43e-08, 2.14e-05, 1.49e-05, 1.22e-03, 7.39e-04, 1.10e-03]    [1.51e-02, 2.17e-02, 1.67e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "20000     [2.01e-02, 2.79e-02, 2.76e-02, 3.93e-07, 4.17e-04, 7.19e-04, 7.03e-04, 4.12e-04, 8.67e-04]    [2.01e-02, 2.79e-02, 2.76e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "21000     [8.13e-03, 1.29e-02, 1.07e-02, 3.63e-06, 1.51e-05, 1.47e-05, 3.36e-04, 2.32e-04, 3.53e-04]    [8.13e-03, 1.29e-02, 1.07e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "22000     [9.50e-03, 1.44e-02, 1.33e-02, 6.96e-06, 9.92e-05, 6.84e-05, 3.05e-04, 2.17e-04, 4.15e-04]    [9.50e-03, 1.44e-02, 1.33e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "23000     [6.52e-03, 9.94e-03, 7.37e-03, 3.61e-06, 8.05e-06, 6.93e-08, 2.16e-04, 1.64e-04, 2.66e-04]    [6.52e-03, 9.94e-03, 7.37e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "24000     [5.97e-03, 9.07e-03, 6.78e-03, 3.71e-06, 9.57e-06, 6.95e-07, 1.79e-04, 1.36e-04, 2.19e-04]    [5.97e-03, 9.07e-03, 6.78e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "25000     [7.62e-03, 1.39e-02, 2.11e-02, 1.31e-06, 5.00e-05, 5.79e-05, 1.57e-04, 1.52e-04, 1.46e-04]    [7.62e-03, 1.39e-02, 2.11e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "26000     [5.11e-03, 7.79e-03, 5.93e-03, 1.62e-06, 4.83e-06, 1.17e-06, 1.33e-04, 1.01e-04, 1.61e-04]    [5.11e-03, 7.79e-03, 5.93e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "27000     [4.75e-03, 7.26e-03, 5.56e-03, 3.66e-06, 5.25e-06, 8.31e-07, 1.19e-04, 9.25e-05, 1.46e-04]    [4.75e-03, 7.26e-03, 5.56e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "28000     [4.88e-03, 7.88e-03, 6.24e-03, 1.25e-08, 2.88e-05, 3.03e-05, 1.22e-04, 8.62e-05, 1.73e-04]    [4.88e-03, 7.88e-03, 6.24e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "29000     [4.20e-03, 6.46e-03, 5.02e-03, 6.38e-07, 2.19e-06, 1.22e-06, 9.98e-05, 7.54e-05, 1.22e-04]    [4.20e-03, 6.46e-03, 5.02e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "30000     [6.46e-03, 8.36e-03, 7.49e-03, 1.15e-05, 1.11e-04, 1.50e-04, 9.41e-05, 6.29e-05, 1.27e-04]    [6.46e-03, 8.36e-03, 7.49e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "31000     [2.56e-02, 1.18e-02, 1.85e-02, 5.91e-05, 3.22e-04, 4.22e-05, 1.65e-04, 9.00e-05, 1.68e-04]    [2.56e-02, 1.18e-02, 1.85e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "32000     [3.52e-03, 5.52e-03, 4.40e-03, 2.79e-06, 5.30e-06, 3.27e-07, 7.51e-05, 5.83e-05, 9.61e-05]    [3.52e-03, 5.52e-03, 4.40e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "33000     [3.48e-03, 5.32e-03, 4.46e-03, 2.39e-07, 1.94e-05, 1.06e-06, 6.55e-05, 4.34e-05, 7.55e-05]    [3.48e-03, 5.32e-03, 4.46e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "34000     [3.14e-03, 5.02e-03, 3.97e-03, 1.07e-06, 4.26e-06, 1.18e-09, 6.21e-05, 4.56e-05, 7.44e-05]    [3.14e-03, 5.02e-03, 3.97e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "35000     [2.98e-03, 4.81e-03, 3.80e-03, 1.13e-06, 3.35e-06, 2.20e-07, 5.70e-05, 4.21e-05, 6.72e-05]    [2.98e-03, 4.81e-03, 3.80e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "36000     [2.91e-03, 4.67e-03, 3.83e-03, 5.09e-06, 4.97e-06, 3.41e-07, 5.51e-05, 4.33e-05, 6.75e-05]    [2.91e-03, 4.67e-03, 3.83e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "37000     [4.89e-03, 5.34e-03, 7.47e-03, 2.05e-05, 1.12e-04, 4.02e-05, 4.82e-05, 4.56e-05, 5.69e-05]    [4.89e-03, 5.34e-03, 7.47e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "38000     [3.77e-03, 7.48e-03, 1.22e-02, 4.37e-06, 1.04e-06, 7.43e-05, 5.97e-05, 5.96e-05, 5.22e-05]    [3.77e-03, 7.48e-03, 1.22e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "39000     [3.71e-03, 4.68e-03, 4.67e-03, 5.37e-06, 5.60e-06, 3.01e-06, 4.89e-05, 3.01e-05, 5.17e-05]    [3.71e-03, 4.68e-03, 4.67e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "40000     [1.78e-02, 3.23e-02, 7.12e-02, 8.60e-05, 1.61e-04, 8.14e-04, 4.01e-04, 4.34e-04, 5.83e-04]    [1.78e-02, 3.23e-02, 7.12e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "41000     [2.56e-03, 4.41e-03, 4.30e-03, 7.92e-06, 7.67e-06, 6.75e-06, 3.93e-05, 2.24e-05, 4.26e-05]    [2.56e-03, 4.41e-03, 4.30e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "42000     [4.11e-03, 5.69e-03, 9.22e-03, 8.88e-05, 6.69e-06, 4.22e-06, 7.09e-05, 6.99e-05, 8.01e-05]    [4.11e-03, 5.69e-03, 9.22e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "43000     [2.29e-03, 3.88e-03, 3.04e-03, 1.29e-07, 2.05e-08, 1.02e-06, 3.88e-05, 2.91e-05, 5.09e-05]    [2.29e-03, 3.88e-03, 3.04e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "44000     [2.73e-02, 1.22e-02, 6.78e-02, 1.63e-04, 8.66e-04, 4.00e-05, 1.45e-04, 5.77e-04, 1.71e-04]    [2.73e-02, 1.22e-02, 6.78e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "45000     [4.18e-03, 1.19e-02, 1.65e-02, 2.36e-06, 1.24e-05, 1.61e-04, 1.18e-04, 1.23e-04, 2.00e-04]    [4.18e-03, 1.19e-02, 1.65e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "46000     [4.25e-03, 5.61e-03, 2.08e-02, 1.06e-04, 2.78e-07, 1.80e-04, 1.62e-04, 2.24e-04, 1.87e-04]    [4.25e-03, 5.61e-03, 2.08e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "47000     [1.82e-03, 3.35e-03, 2.82e-03, 1.14e-08, 3.27e-06, 3.12e-06, 2.57e-05, 1.83e-05, 2.72e-05]    [1.82e-03, 3.35e-03, 2.82e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "48000     [1.96e-03, 3.38e-03, 3.01e-03, 2.80e-06, 1.82e-05, 4.19e-06, 2.58e-05, 1.59e-05, 3.10e-05]    [1.96e-03, 3.38e-03, 3.01e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "49000     [4.68e-03, 5.37e-03, 5.07e-03, 3.28e-06, 3.16e-05, 2.61e-05, 4.71e-05, 5.28e-05, 8.85e-05]    [4.68e-03, 5.37e-03, 5.07e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "50000     [1.63e-03, 3.03e-03, 2.32e-03, 4.34e-07, 3.43e-06, 8.97e-08, 2.44e-05, 1.70e-05, 2.69e-05]    [1.63e-03, 3.03e-03, 2.32e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "51000     [1.62e-03, 3.12e-03, 2.76e-03, 2.39e-06, 4.45e-07, 9.72e-07, 2.98e-05, 2.60e-05, 3.74e-05]    [1.62e-03, 3.12e-03, 2.76e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "52000     [9.24e-03, 3.03e-02, 4.85e-02, 3.32e-07, 1.05e-05, 7.33e-04, 1.74e-04, 2.48e-04, 3.18e-04]    [9.24e-03, 3.03e-02, 4.85e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "53000     [1.52e-02, 4.36e-02, 1.05e-01, 7.52e-05, 2.78e-06, 1.02e-03, 3.93e-04, 5.37e-04, 4.69e-04]    [1.52e-02, 4.36e-02, 1.05e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "54000     [2.03e-03, 4.26e-03, 3.27e-03, 1.11e-05, 3.76e-06, 6.37e-05, 1.94e-05, 2.76e-05, 3.74e-05]    [2.03e-03, 4.26e-03, 3.27e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "55000     [1.35e-02, 4.76e-02, 1.02e-01, 4.85e-05, 6.58e-06, 6.48e-04, 4.74e-04, 7.19e-04, 8.11e-04]    [1.35e-02, 4.76e-02, 1.02e-01, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "56000     [1.36e-03, 2.68e-03, 2.00e-03, 6.72e-07, 7.48e-07, 2.58e-06, 1.78e-05, 1.37e-05, 1.96e-05]    [1.36e-03, 2.68e-03, 2.00e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "57000     [1.33e-03, 2.60e-03, 1.93e-03, 6.23e-07, 2.43e-07, 1.41e-07, 1.84e-05, 1.45e-05, 2.09e-05]    [1.33e-03, 2.60e-03, 1.93e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "58000     [1.37e-03, 2.56e-03, 2.01e-03, 1.53e-06, 4.32e-07, 9.57e-07, 1.93e-05, 1.84e-05, 2.28e-05]    [1.37e-03, 2.56e-03, 2.01e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "59000     [2.23e-03, 2.81e-03, 2.79e-03, 1.10e-05, 2.42e-05, 1.02e-06, 2.28e-05, 1.38e-05, 2.10e-05]    [2.23e-03, 2.81e-03, 2.79e-03, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "60000     [6.06e-03, 1.53e-02, 2.97e-02, 5.05e-07, 8.52e-05, 2.69e-04, 9.39e-05, 2.05e-04, 1.96e-04]    [6.06e-03, 1.53e-02, 2.97e-02, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00, 0.00e+00]    []  \n",
            "\n",
            "Best model at step 57000:\n",
            "  train loss: 5.91e-03\n",
            "  test loss: 5.86e-03\n",
            "  test metric: []\n",
            "\n",
            "'train' took 362.351454 s\n",
            "\n"
          ],
          "name": "stdout"
        }
      ]
    },
    {
      "cell_type": "markdown",
      "metadata": {
        "id": "xWKCLjyETSPG"
      },
      "source": [
        "Plots"
      ]
    },
    {
      "cell_type": "code",
      "metadata": {
        "id": "6XsrIWXjTTJb",
        "outputId": "73fb7842-92b2-458f-8d14-b5479c100f5c",
        "colab": {
          "base_uri": "https://localhost:8080/",
          "height": 608
        }
      },
      "source": [
        "# reopen saved data using callbacks in fnamevar\n",
        "lines = open(fnamevar, \"r\").readlines()\n",
        "\n",
        "# read output data in fnamevar (this line is a long story...)\n",
        "Chat = np.array(\n",
        "    [\n",
        "        np.fromstring(\n",
        "            min(re.findall(re.escape(\"[\") + \"(.*?)\" + re.escape(\"]\"), line), key=len),\n",
        "            sep=\",\",\n",
        "        )\n",
        "        for line in lines\n",
        "    ]\n",
        ")\n",
        "\n",
        "l, c = Chat.shape\n",
        "\n",
        "plt.plot(range(l), Chat[:, 0], \"r-\")\n",
        "plt.plot(range(l), Chat[:, 1], \"k-\")\n",
        "plt.plot(range(l), Chat[:, 2], \"g-\")\n",
        "plt.plot(range(l), np.ones(Chat[:, 0].shape) * C1true, \"r--\")\n",
        "plt.plot(range(l), np.ones(Chat[:, 1].shape) * C2true, \"k--\")\n",
        "plt.plot(range(l), np.ones(Chat[:, 2].shape) * C3true, \"g--\")\n",
        "plt.legend([\"C1hat\", \"C2hat\", \"C3hat\", \"True C1\", \"True C2\", \"True C3\"], loc=\"right\")\n",
        "plt.xlabel(\"Epoch\")\n",
        "plt.show()\n",
        "\n",
        "\n",
        "yhat = model.predict(observe_t)\n",
        "\n",
        "plt.plot(observe_t, ob_y, \"-\", observe_t, yhat, \"--\")\n",
        "plt.xlabel(\"Time\")\n",
        "plt.legend([\"x\", \"y\", \"z\", \"xh\", \"yh\", \"zh\"])\n",
        "plt.title(\"Training data\")\n",
        "plt.show()\n"
      ],
      "execution_count": null,
      "outputs": [
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        },
        {
          "output_type": "stream",
          "text": [
            "Predicting...\n",
            "'predict' took 0.020893 s\n",
            "\n"
          ],
          "name": "stdout"
        },
        {
          "output_type": "display_data",
          "data": {
            "image/png": 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\n",
            "text/plain": [
              "<Figure size 432x288 with 1 Axes>"
            ]
          },
          "metadata": {
            "tags": [],
            "needs_background": "light"
          }
        }
      ]
    }
  ]
}